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Towards the Next Generation Intelligent BPM – In the Era of Big Data

  • Conference paper
Business Process Management

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8094))

Abstract

Big data opens a new dimension, space, to offer the advantage of gleaning intelligence from data and translating that into business benefits. It will lead to knowledge revolution in all sectors, including Business Process Management (BPM). This paper sheds light on key characteristics of intelligent BPM (iBPM) from an industrial point of view. A big data perspective on iBPM is then proposed, showing the challenges and potential opportunities in attempt to catalyze ideas from insight to application. China Mobile Communications Corporation’s (CMCC) exploring and practice are provided, which also elicit the future research directions for enterprise applications.

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References

  1. Barabászló, A.L.: The Network Takeover. Nature Physics 8(1), 14–16 (2011)

    Google Scholar 

  2. Billsus, D., Pazzani, M.J.: Learning Collaborative Information Filters. In: Proc. of ICML 1998, pp. 46–53 (1998)

    Google Scholar 

  3. Cooper, B.F., Silberstein, A., Tam, E., et al.: Benchmarking Cloud Serving Systems with YCSB. In: Proc. of the 1st ACM Symposium on Cloud Computing, pp. 143–154. ACM (2010)

    Google Scholar 

  4. Dijkman, R.M., Dumas, M., Dongen, B., Uba, R., Mendling, J.: Similarity of Business Process Models: Metrics and Evaluation. Information Systems 36(2), 498–516 (2011)

    Google Scholar 

  5. Ekanayake, C.C., Dumas, M., García-Bañuelos, L., La Rosa, M., ter Hofstede, A.H.M.: Approximate Clone Detection in Repositories of Business Process Models. In: Barros, A., Gal, A., Kindler, E. (eds.) BPM 2012. LNCS, vol. 7481, pp. 302–318. Springer, Heidelberg (2012)

    Google Scholar 

  6. Gschwind, T., Koehler, J., Wong, J.: Applying patterns during business process modeling. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 4–19. Springer, Heidelberg (2008)

    Google Scholar 

  7. Janelle, B.H., Roy Schulte, W.: BPM Suites Evolve into Intelligent BPM Suites, Gartner G00226553 (2011)

    Google Scholar 

  8. Langley, A.: Strategies for Theorizing from Process Data. Academy of Management Review, 691-710 (1999)

    Google Scholar 

  9. Lin, Y.: Semantic Annotation for Process Models. Diss. Trondheim, Norway (2008)

    Google Scholar 

  10. Ritter, D.: Towards a Business Network Management. Enterprise Information Systems of the Future, pp. 149–156. Springer, Heidelberg (2013)

    Google Scholar 

  11. Samantha, S.: Research Index: New BPM Technologies Lead the Way to Achieving Process Adaptability. Gartner 00228461 (2012)

    Google Scholar 

  12. Sarwar, B., Karypis, G., Konstan, J., Riedl, J., et al.: Item-based Collaborative Filtering Recommendation Algorithms. In: Proc. International Conference on World Wide Web 2001, pp. 285–295. ACM (2001)

    Google Scholar 

  13. Tawny, S., Brian, D.J.: Entertainment in the Age of Big Data. Proceedings of the IEEE 100(5), 1404–1408 (2012)

    Google Scholar 

  14. van der Aalst, W.M.P.: A Decade of Business Process Management Conferences: Personal reflections on a Developing Discipline. In: Barros, A., Gal, A., Kindler, E. (eds.) BPM 2012. LNCS, vol. 7481, pp. 1–16. Springer, Heidelberg (2012)

    Google Scholar 

  15. van der Aalst, W.M.P., Weijters, T., Maruster, L.: IEEE Transactions on Knowledge and Data Engineering 16(9), 1128–1142 (2004)

    Google Scholar 

  16. van der Aalst, W.M.P., Dongen, B., Herbst, J., Maruster, L., Schimm, G., Weijters, A.: Workflow mining: A Survey of Issues and Approaches. Data & Knowledge Engineering 47(2), 237–267 (2003)

    Google Scholar 

  17. van der Aalst, W.M.P., Song, M.S.: Mining Social Networks: Uncovering Interaction Patterns in Business Processes. In: Desel, J., Pernici, B., Weske, M. (eds.) BPM 2004. LNCS, vol. 3080, pp. 244–260. Springer, Heidelberg (2004)

    Google Scholar 

  18. Vanhatalo, J., Völzer, H., Koehler, J.: The Refined Process Structure Tree. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 100–115. Springer, Heidelberg (2008)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Management Information System, China Mobile Communications Corporation, Beijing, 100033, China

    Xiang Gao

Authors
  1. Xiang Gao
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Editor information

Editors and Affiliations

  1. Department of Information Engineering and Computer Science, University of Trento, 38123, Povo, TN, Italy

    Florian Daniel

  2. Institute of Information System and Engineering of School Software, Tsinghua University, 100084, Beijing, China

    Jianmin Wang

  3. Business Process Management Research Cluster, University of Innsbruck, 6020, Innsbruck, Austria

    Barbara Weber

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Gao, X. (2013). Towards the Next Generation Intelligent BPM – In the Era of Big Data. In: Daniel, F., Wang, J., Weber, B. (eds) Business Process Management. Lecture Notes in Computer Science, vol 8094. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40176-3_2

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  • DOI: https://doi.org/10.1007/978-3-642-40176-3_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40175-6

  • Online ISBN: 978-3-642-40176-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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